{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "\n",
    "Copyright 2018 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferring thermal resistance and thermal mass of a wall\n",
    "\n",
    "This case study is based on Gori, Marincioni, Biddulph, Elwell, \"Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces\", *Energy and Buildings*, Volume 135, 15 January 2017, Pages 398-409, [which I downloaded here](https://www.sciencedirect.com/science/article/pii/S0378778816313056).\n",
    "    \n",
    "The authors put their paper under a Creative Commons license, and [make their data available here](http://discovery.ucl.ac.uk/1526521).  I thank them for their commitment to open, reproducible science, which made this case study possible.\n",
    "\n",
    "The goal of their paper is to model the thermal behavior of a wall as a step toward understanding the \"performance gap between the expected energy use of buildings and their measured energy use\".  The wall they study is identified as the exterior wall of an office building in central London, [not unlike this one](https://www.google.com/maps/@51.5269375,-0.1303666,3a,75y,90h,88.17t/data=!3m6!1e1!3m4!1sAoAXzN0mbGF9acaVEgUdDA!2e0!7i13312!8i6656).\n",
    "\n",
    "The following figure shows the scenario and their model:\n",
    "\n",
    "![Figure 2](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr2.jpg)\n",
    "\n",
    "On the interior and exterior surfaces of the wall, they measure temperature and heat flux over a period of three days.  They model the wall using two thermal masses connected to the surfaces, and to each other, by thermal resistors.\n",
    "\n",
    "The primary methodology of the paper is a Bayesian method for inferring the parameters of the system (two thermal masses and three thermal resistances).\n",
    "\n",
    "The primary result is a comparison of two models: the one shown here with two thermal masses, and a simpler model with only one thermal mass.  They find that the two-mass model is able to reproduce the measured fluxes substantially better.\n",
    "\n",
    "Tempting as it is, I will not replicate their method for estimating the parameters.  Rather, I will\n",
    "\n",
    "1. Implement their model and run it with their estimated parameters, to replicate the results, and \n",
    "\n",
    "2. Use SciPy's `leastsq` to see if I can find parameters that yield lower errors (root mean square).\n",
    "\n",
    "`leastsq` is a wrapper for some venerable FORTRAN code that runs [\"a modification of the Levenberg-Marquardt algorithm\"](https://www.math.utah.edu/software/minpack/minpack/lmdif.html), which is one of my favorites ([really](http://allendowney.com/research/model)).\n",
    "\n",
    "Implementing their model in the ModSimPy framework turns out to be straightforward.  The simulations run fast enough even when we carry units through the computation.  And the results are visually similar to the ones in the original paper.\n",
    "\n",
    "I find that `leastsq` is not able to find parameters that yield substantially better results, which suggest that the estimates in the paper are at least locally optimal.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data\n",
    "\n",
    "First I'll load the units we need from Pint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "second"
      ],
      "text/latex": [
       "$\\mathrm{second}$"
      ],
      "text/plain": [
       "<Unit('second')>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = UNITS.meter\n",
    "K = UNITS.kelvin\n",
    "W = UNITS.watt\n",
    "J = UNITS.joule\n",
    "degC = UNITS.celsius\n",
    "s = UNITS.second"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q_in</th>\n",
       "      <th>Q_out</th>\n",
       "      <th>T_int</th>\n",
       "      <th>T_ext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-05 16:30:00</th>\n",
       "      <td>10.994</td>\n",
       "      <td>6.840</td>\n",
       "      <td>16.92</td>\n",
       "      <td>14.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05 16:35:00</th>\n",
       "      <td>10.952</td>\n",
       "      <td>6.012</td>\n",
       "      <td>16.92</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05 16:40:00</th>\n",
       "      <td>10.882</td>\n",
       "      <td>7.040</td>\n",
       "      <td>16.93</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05 16:45:00</th>\n",
       "      <td>10.798</td>\n",
       "      <td>8.880</td>\n",
       "      <td>16.93</td>\n",
       "      <td>14.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-05 16:50:00</th>\n",
       "      <td>10.756</td>\n",
       "      <td>10.491</td>\n",
       "      <td>16.94</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Q_in   Q_out  T_int  T_ext\n",
       "2014-10-05 16:30:00  10.994   6.840  16.92  14.68\n",
       "2014-10-05 16:35:00  10.952   6.012  16.92  14.69\n",
       "2014-10-05 16:40:00  10.882   7.040  16.93  14.66\n",
       "2014-10-05 16:45:00  10.798   8.880  16.93  14.59\n",
       "2014-10-05 16:50:00  10.756  10.491  16.94  14.50"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/DataOWall.csv', parse_dates=[0], index_col=0, header=0, skiprows=[1,2])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index contains Pandas `Timestamp` objects, which is good for dealing with real-world dates and times, but not as good for running the simulations, so I'm going to convert to seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-10-05 16:30:00')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timestamp_0 = get_first_label(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtracting the first `Timestamp` yields `Timedelta` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days 00:00:00', '0 days 00:05:00', '0 days 00:10:00',\n",
       "                '0 days 00:15:00', '0 days 00:20:00', '0 days 00:25:00',\n",
       "                '0 days 00:30:00', '0 days 00:35:00', '0 days 00:40:00',\n",
       "                '0 days 00:45:00',\n",
       "                ...\n",
       "                '2 days 23:10:00', '2 days 23:15:00', '2 days 23:20:00',\n",
       "                '2 days 23:25:00', '2 days 23:30:00', '2 days 23:35:00',\n",
       "                '2 days 23:40:00', '2 days 23:45:00', '2 days 23:50:00',\n",
       "                '2 days 23:55:00'],\n",
       "               dtype='timedelta64[ns]', length=864, freq=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_deltas = data.index - timestamp_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can convert to seconds and replace the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q_in</th>\n",
       "      <th>Q_out</th>\n",
       "      <th>T_int</th>\n",
       "      <th>T_ext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.994</td>\n",
       "      <td>6.840</td>\n",
       "      <td>16.92</td>\n",
       "      <td>14.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>10.952</td>\n",
       "      <td>6.012</td>\n",
       "      <td>16.92</td>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>10.882</td>\n",
       "      <td>7.040</td>\n",
       "      <td>16.93</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>10.798</td>\n",
       "      <td>8.880</td>\n",
       "      <td>16.93</td>\n",
       "      <td>14.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>10.756</td>\n",
       "      <td>10.491</td>\n",
       "      <td>16.94</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Q_in   Q_out  T_int  T_ext\n",
       "0     10.994   6.840  16.92  14.68\n",
       "300   10.952   6.012  16.92  14.69\n",
       "600   10.882   7.040  16.93  14.66\n",
       "900   10.798   8.880  16.93  14.59\n",
       "1200  10.756  10.491  16.94  14.50"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = time_deltas.days * 86400 + time_deltas.seconds\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timesteps are all 5 minutes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(np.diff(data.index) == 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mark the columns of the `Dataframe` with units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Q_in.units = W / m**2\n",
    "data.Q_out.units = W / m**2\n",
    "data.T_int.units = degC\n",
    "data.T_ext.units = degC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the measured fluxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data.Q_in, color='C2')\n",
    "plot(data.Q_out, color='C0')\n",
    "decorate(xlabel='Time (s)',\n",
    "         ylabel='Heat flux (W/$m^2$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the measured temperatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data.T_int, color='C2')\n",
    "plot(data.T_ext, color='C0')\n",
    "decorate(xlabel='Time (s)',\n",
    "         ylabel='Temperature (degC)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Params and System objects\n",
    "\n",
    "Here's a `Params` object with the [estimated parameters from the paper](https://www.sciencedirect.com/science/article/pii/S0378778816313056#tbl0005)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>0.076 kelvin * meter ** 2 / watt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>0.272 kelvin * meter ** 2 / watt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R3</th>\n",
       "      <td>0.078 kelvin * meter ** 2 / watt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>212900.0 joule / kelvin / meter ** 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C2</th>\n",
       "      <td>113100.0 joule / kelvin / meter ** 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "R1        0.076 kelvin * meter ** 2 / watt\n",
       "R2        0.272 kelvin * meter ** 2 / watt\n",
       "R3        0.078 kelvin * meter ** 2 / watt\n",
       "C1    212900.0 joule / kelvin / meter ** 2\n",
       "C2    113100.0 joule / kelvin / meter ** 2\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = Params(\n",
    "    R1 = 0.076 * m**2 * K / W,\n",
    "    R2 = 0.272 * m**2 * K / W,\n",
    "    R3 = 0.078 * m**2 * K / W,\n",
    "    C1 = 212900 * J / m**2 / K,\n",
    "    C2 = 113100 * J / m**2 / K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll pass the `Params` object `make_system`, which computes `init`, packs the parameters into `Series` objects, and computes the interpolation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_system(params, data):\n",
    "    \"\"\"Makes a System object for the given conditions.\n",
    "    \n",
    "    params: Params object\n",
    "    \n",
    "    returns: System object\n",
    "    \"\"\"\n",
    "    R1, R2, R3, C1, C2 = params\n",
    "    \n",
    "    init = State(T_C1 = Quantity(16.11, degC),\n",
    "                 T_C2 = Quantity(15.27, degC))\n",
    "    \n",
    "    ts = data.index\n",
    "    t_end = ts[-1] * s\n",
    "    \n",
    "    return System(init=init,\n",
    "                  R=Series([R1, R2, R3]),\n",
    "                  C=Series([C1, C2]),\n",
    "                  T_int_func=interpolate(data.T_int),\n",
    "                  T_ext_func=interpolate(data.T_ext),\n",
    "                  unit_temp=degC,\n",
    "                  t_end=t_end, ts=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a `System` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>init</th>\n",
       "      <td>T_C1    16.11 degC\n",
       "T_C2    15.27 degC\n",
       "dtype: o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0    0.076 kelvin * meter ** 2 / watt\n",
       "1    0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0    212900.0 joule / kelvin / meter ** 2\n",
       "1   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_int_func</th>\n",
       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_ext_func</th>\n",
       "      <td>&lt;function interpolate.&lt;locals&gt;.wrapper at 0x7f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_temp</th>\n",
       "      <td>degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_end</th>\n",
       "      <td>258900 second</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</th>\n",
       "      <td>Int64Index([     0,    300,    600,    900,   ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "init          T_C1    16.11 degC\n",
       "T_C2    15.27 degC\n",
       "dtype: o...\n",
       "R             0    0.076 kelvin * meter ** 2 / watt\n",
       "1    0.2...\n",
       "C             0    212900.0 joule / kelvin / meter ** 2\n",
       "1   ...\n",
       "T_int_func    <function interpolate.<locals>.wrapper at 0x7f...\n",
       "T_ext_func    <function interpolate.<locals>.wrapper at 0x7f...\n",
       "unit_temp                                                  degC\n",
       "t_end                                             258900 second\n",
       "ts            Int64Index([     0,    300,    600,    900,   ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = make_system(params, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the interpolation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.68, 14.684999999999999, 14.69)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.T_ext_func(0), system.T_ext_func(150), system.T_ext_func(300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the model\n",
    "\n",
    "Next we need a slope function that takes instantaneous values of the two internal temperatures and computes their time rates of change.\n",
    "\n",
    "The slope function gets called two ways.\n",
    "\n",
    "* When we call it directly, `state` is a `State` object and the values it contains have units.\n",
    "\n",
    "* When `run_ode_solver` calls it, `state` is an array and the values it contains don't have units.\n",
    "\n",
    "In the second case, we have to apply the units before attempting the computation.  `require_units` applies units if necessary:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function computes the fluxes between the four zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_flux(state, t, system):\n",
    "    \"\"\"Compute the fluxes between the walls surfaces and the internal masses.\n",
    "    \n",
    "    state: State with T_C1 and T_C2\n",
    "    t: time in seconds\n",
    "    system: System with interpolated measurements and the R Series\n",
    "    \n",
    "    returns: Series of fluxes\n",
    "    \"\"\"    \n",
    "    # unpack the temperatures\n",
    "    T_C1, T_C2 = state\n",
    "        \n",
    "    # compute a series of temperatures from inside out\n",
    "    T_int = system.T_int_func(t)\n",
    "    T_ext = system.T_ext_func(t)\n",
    "    \n",
    "    T = [require_units(T_int, system.unit_temp),\n",
    "         require_units(T_C1, system.unit_temp),\n",
    "         require_units(T_C2, system.unit_temp),\n",
    "         require_units(T_ext, system.unit_temp)]\n",
    "    \n",
    "    # compute differences of adjacent temperatures\n",
    "    T_diff = np.diff(T)\n",
    "\n",
    "    # compute fluxes between adjacent compartments\n",
    "    Q = T_diff / system.R\n",
    "    return Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test it like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -10.657894736842135 delta_degC * watt / kelvin...\n",
       "1    -3.0882352941176463 delta_degC * watt / kelvin...\n",
       "2    -7.564102564102562 delta_degC * watt / kelvin ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_flux(system.init, 0, system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a slope function that computes derivatives of `T_C1` and `T_C2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def slope_func(state, t, system):\n",
    "    \"\"\"Compute derivatives of the state.\n",
    "    \n",
    "    state: position, velocity\n",
    "    t: time\n",
    "    system: System object\n",
    "    \n",
    "    returns: derivatives of y and v\n",
    "    \"\"\"\n",
    "    Q = compute_flux(state, t, system)\n",
    "\n",
    "    # compute the net flux in each node\n",
    "    Q_diff = np.diff(Q)\n",
    "        \n",
    "    # compute the rate of change of temperature\n",
    "    dQdt = Q_diff / system.C\n",
    "    return dQdt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the slope function with the initial conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3.555499973097458e-05 delta_degC * watt / joule\n",
       "1    -3.844086774341106e-05 delta_degC * watt / joule\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slopes = slope_func(system.init, system.ts[1], system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.11 degC 3.555499973097458e-05 delta_degC * second * watt / joule\n",
      "15.27 degC -3.844086774341106e-05 delta_degC * second * watt / joule\n"
     ]
    }
   ],
   "source": [
    "for y, slope in zip(system.init, slopes):\n",
    "    print(y, slope*s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the simulation, generating estimates for the time steps in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>message</th>\n",
       "      <td>The solver successfully reached the end of the...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "success                                                 True\n",
       "message    The solver successfully reached the end of the...\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results, details = run_ode_solver(system, slope_func)\n",
    "details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the results look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_C1</th>\n",
       "      <th>T_C2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.11 degC</td>\n",
       "      <td>15.27 degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2589</th>\n",
       "      <td>16.19153931824483 degC</td>\n",
       "      <td>15.119152775835897 degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5178</th>\n",
       "      <td>16.2528855986394 degC</td>\n",
       "      <td>14.860249399364765 degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7767</th>\n",
       "      <td>16.294553283278788 degC</td>\n",
       "      <td>14.58496288165028 degC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>16.317098239731408 degC</td>\n",
       "      <td>14.348725465924332 degC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          T_C1                     T_C2\n",
       "0                   16.11 degC               15.27 degC\n",
       "2589    16.19153931824483 degC  15.119152775835897 degC\n",
       "5178     16.2528855986394 degC  14.860249399364765 degC\n",
       "7767   16.294553283278788 degC   14.58496288165028 degC\n",
       "10356  16.317098239731408 degC  14.348725465924332 degC"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_results(results, data):\n",
    "    plot(data.T_int, color='C2')\n",
    "    plot(results.T_C1, color='C3')\n",
    "    plot(results.T_C2, color='C1')\n",
    "    plot(data.T_ext, color='C0')\n",
    "    decorate(xlabel='Time (s)',\n",
    "             ylabel='Temperature (degC)')\n",
    "    \n",
    "plot_results(results, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results are similar to what's in the paper:\n",
    "\n",
    "![Figure 5](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr5.jpg).  \n",
    "\n",
    "To get the estimated fluxes, we have to go through the results and basically do the flux calculation again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q_in</th>\n",
       "      <th>Q_out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.6579</td>\n",
       "      <td>7.5641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2589</th>\n",
       "      <td>9.93106</td>\n",
       "      <td>12.6725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5178</th>\n",
       "      <td>9.30414</td>\n",
       "      <td>17.6391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7767</th>\n",
       "      <td>8.90193</td>\n",
       "      <td>17.1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>8.79081</td>\n",
       "      <td>16.4555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Q_in    Q_out\n",
       "0      10.6579   7.5641\n",
       "2589   9.93106  12.6725\n",
       "5178   9.30414  17.6391\n",
       "7767   8.90193  17.1162\n",
       "10356  8.79081  16.4555"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def recompute_fluxes(results, system):\n",
    "    \"\"\"Compute fluxes between wall surfaces and internal masses.\n",
    "    \n",
    "    results: Timeframe with T_C1 and T_C2\n",
    "    system: System object\n",
    "    \n",
    "    returns: Timeframe with Q_in and Q_out\n",
    "    \"\"\"\n",
    "    Q_frame = TimeFrame(index=results.index, columns=['Q_in', 'Q_out'])\n",
    "    \n",
    "    for t, row in results.iterrows():\n",
    "        Q = compute_flux(row, t, system)\n",
    "        \n",
    "        Q_frame.row[t] = (-Q[0].magnitude, \n",
    "                          -Q[2].magnitude)\n",
    "        \n",
    "    return Q_frame\n",
    "            \n",
    "Q_frame = recompute_fluxes(results, system)\n",
    "Q_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the estimates compare to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jgVMpb3lVvcy8rPfVsq5xFQAnx3tPe50ic6LJ7NjAsAzZNTY2znh+7dq1qe2pWJiRkRFqa2tTnUCqqqoQQmBZFrt2zR2atb/ryUhozuezgRIoxaIQCE+ntcZiUXRLCtSJU93EYjGCEelJrezonNf7aZrGsuTd+omAbMXicrm45NJLWPraVRxbJlOj/2PP3YyOqpY6mTI4PEjYO1ugGs9w9EyBshNZQtEpegKyg8TGJWJGyHZJsmTg/3vyuwyFlBeVDUKxKWJmHJfDxeiw/H+a7kEBCCFS23ai0pYtW1JhQIDNm2Xiy86dO+ds6mx/17898uRp5QPZQgmUYlEYSZvW6Yu60ZICFU1EeeSRR4gh1zg6WtrnfP1cLKuRApU++PDBQ49y35H/xUpbHrnzzjtVhl+G7Ok7gKVBpT6dRpy+UD4bt8OV2l7VID2lhGZysEfWO3XUzAwBtle3pLYPDqlatmzQNyFT+VsrmxkalJGL2R5UY2MjawKtACztraGyspKLL754xjErV66krq6OQCDA4cOnrxNWeaZvRiajufGilEApFoVQWjFfU6AKLXlDZmqwbfs2Eg4pIBUe/7zfsy15cTs0cizV0PLw8PHTjhsYGKCnp+e0/Ypz82BAZldOmlN8+uo/4ZLWC2YMtZtNeuKE3Xw04TB5sU8W+M4u6L1h5TWp7akc3YWXG3at2ZIK2WVF0zQaGmaGZTVN49XLrmDdwWZqx31ceeWVOJ3O04659NJLATn6ZjYObTpVPVf1bEqgFIuC3ar/w5e/m+D4ZCrE5/K6iLkSmLqF3+Gbcw3jTFzUMl2bYRf0xq3TG5QC7N69O1PTFcCljRdwVcdmPr/1z85ZlHlJ6wUA3LDy1QDEnSYjUbkWcunSC2Vm4KlTWJaF1+XllrWvBWbexCgyxx5J48WNZVnU19efJj4gw3wOU8fr9XLZZZfN+V6XXHIJuq5z6NAhRkZGGBkZ4fjx40xOTiIaV3JB81r+4MLfy1nGrEozVywKds89Bw4ikQgOn7z7qmusZ2pIXryW1Zw7ey+dlsom3nPx7/OTl+7hREB6SAnzdIGysNi3bx8333wzDofjtOcVZ8aZ0Ik7TN696a3zfs2fX/MRIvEIFW4/PqeXqXiYBCa6plPh8vPwww/z/PPPc80113D99denSgqm4sqDygb2epAZlefC7PCeTWdnJzfffDNLlizB4/HMeUxFRQXr169n3759fOc7021Sa2tr+cQnPsGXr/t0lq2fifKgFIuCLVBW8qSxF9CNYBfeBhnWa6lunvvFZ2FNwwoAupMhvr5ZrXQA6pobCIVCc8bRFWcmGAoS12Usdklt0zmOnsapO6hwy++0xjvdlNTr8HDs2DGef/55AJ599lkGBwfxJ2trQlElUNnAFqjElEx+mJ0gYaNpGldeeSUrVqw46/tdddVVOBwOdF2npqYGl8vF2NjYooTNlUApFoVIUqASSYGq9053Hj/ol+KSfjGbL8tq2vA4PRwPnGJv/yv0TpwuUM/U78fULPbs2ZOJ6WVL72AfaOCwdFxO17lfMAeNaWsTTlPnvvvuA6C6uhrTNHnwwQfxOaVAPXjosZw2Hi0X7FBpNCTD6mfyoOZLW1sbn/vc5/jCF77Apz71qVQyhWEY52foPFACpVgUbA8qNiVPmqbK02tpMhEon8vL1uVXAPB417YzHnesYwTDMFL1Vopzc2pI1iZ5tLmLcudDe3VrajsajDA2NkZLSwsf/OAH8fv9HDt2jKHe6ck5X/jfv8/cYAUw7UFNjcvMujN5UAvB6XTO6EQBcPDg3HPasokSKMWiMFug6qpPn91U7Vm4QAFsatkAwHPdp2ca2Uz5YyQSidRkWMW56RmRtUtVzvknrsymM22MuMPU0XWdN7/5zVRWVnLjjTcC0LV9+kKnCnbPH1ugQuOytvB8PajZdHZ24nK56O/vJxDI7cBCJVCKRcEWqEhIejBVVVX8360fn3FMJh4UyLlEuqanevnZ46znQoX55o89zrvOW5Pxe1zZPj0CxRnXufbaa1myZAkAmzZtYtmyZUQmw7yl8rrUcapmLXN6JwbY3SdvwvQY1NTU4HZn7gHPhdPpZNUq2QEk116UEijFomD3BgtPyru76upqmmeF+Woy9KAq3H6uXXFV6nGdf9o7e8/Fb0tt604Hx48fT/UWU5yd4aAsrm6qzLzGxe/2UZHM0ru08yK2bNmSek7TNG666SYA+g/1pNaictWVoBz4l+0/Tm27Yo6shPfmYrHCfEqgFIuC3U4lOCHDDlVVVbRWNlOV1nyyJtkdORPedsEtqe0Kl5+/fPVHecPa67ll7XWpz2huk3fuAwOnJ1IoTmc0KsM3S2tbznHk2fnGTZ/nfZe8nQ/d8N4ZrXQAWltbcbvdBAIBKpOZf4GIuoHIlKOjJ1LbzoSe9fCejS1QXV1dRKPRcxydOUqgFDnHsqxUo9BQmkBpmjajk0C6WC2UBl8dTl2W9dV6q9m8dCPvueRtydobeQc/UDVOxBVneHg4488pF6LRKEFNhmNXNC07x9FnZ0llE7esfe1p4gRyOqs9p8iDDEXZhaaKhZOelKKh5cyDqqiooL29nUQiwdGjR3PyGaAESrEI2GtDDt3B5IS8+FRVyXDe69ZcC8iTyalnXkSraRp/9qr3ccua63jz+ptnPGcPzHs5fpjDq4YYGVFNSc/F6OgoUZcsCWipzM1FzsYWKEdMdiMYCwdSnUcUC8PuhXhRUNY25UqgANasWQPkNsynOkkoco4d3nPrLkzTxOfz4XLJE6nOV8M3b/7iGecLLYSrl23m6mWbT9vfUFHHkdHpHn1KoM7NqYEeYu4EDktPdRzPFe3tsvmsORUHJ3zr2X/D7XDx72/+Jp4s/C7KhX95/icYw9KbiQ5P4SD7GXzpCCF47LHHOHjwIJZl5aTdkfKgFDnHDu85k80lbe/JZlltW07v0pfN6qA9NDx0hiMVNsag7D5e76zBcR6e7XywPajoxPSAyWgilprAqzg3oegUj3c9l3psRUy8Xi8+39n7Jp4Pzc3N1NTUEAwGZ4yKzyZKoBQ5J5rM4LO7H1dXZ54MkQmbl26c8Xg4MEIiMXdTWYWke0LWIy31Lrz91EKprKyktrYWLTo7vTw3DUhLkan4zAJ0R0Kjpibz8oD5oGkab3rTm7juuuty9llKoBQ5x/ag7A7mlZWZJ0Nkgj3B1SamJ3J2x1cq9EWll7m8+vQR7rmgra2NqsmZDUvt2jnFuZktUBraotwIrlixgq1bt86ZAJMNlEApck5slkAttgcFMxf6T7aNqXWoczCUkAK+qm75OY7MDm1tbbhjTlY7pochhuOqLdV8CcdOF/N8nGfZRgmUIufYHhTJqNrsNajF4K9fe1tqO+xVqeZnIxSbIqxF0UzoqF8cD8pOlFg6OF1krTyo+TPbgwIlUArFvJgWKLnGkI8Tp95XS3VandXQkEqUOBP2+G5nQqdqkcKxra2t6LrO8MAQ13RcDiiBWgjpXeAv1QWgBEqhmBe2QFlxOVsoHx4UQMIyU9v9o4NnObK82dN3AABHQqeiIvNGsQvB6XTS0iI7ViQism6uR2XxzRtboK5ZfgUtQemFKoFSKOaBLVBmTMb48iVQZppADQWUBzUXQ8ER/vXFn8oHujbnqPBckUo3D0nP6X8O/Ib+SXUjMR9sb9Pr9DA+Pg4ogVIo5oWdZm7GTXR98e7KZ/Ppq/4ktT06GSAej+fFjkIm3WsJu2OL+tn2OlTtxHTtTndAjd+YD3YzZo/DpQRKoVgI6Wnmdg++fHBx6wWsb1oNQMJhMjo6mhc7CpmRqfyl39se1MTJMV6dHEI5GQ3mzZ5iwj7HNFMjkUjg9XqzPmYjH2TsvwshdGAd0IzMz+oDDhuGoYa5KGYQSzt58hXes6lyy0X/hMNieHg4p73KipGx8Hhq+2ouXNTPrq+vx+fzEQwGqUKuR01ElEDNh1SUIhlGLwXvCTIQKCHEVuCTwA1A+tXGAkaFEA8D/2IYxrPZMVFR7KR7UPk+cfxuGT5K6KaqhZqDsSk5YmPJQBWrVyxODZSNpmm0t7dz6NAh4iH5m5mIqs7m82H2Om+uu0gsFvMO8Qkh1gghHgV+BBwF3gq0AV7AD3QC7wN6gDuFEI8JIdZm22BF8ZEefljsLhKzqUx2NjeTHpRiJpMxmWLuSOTnu7LXoSLjcmih8qDmh32OxZMZkPmOVGSLhXhQ/wV81TCMB87wfHfy734hxF8Cb06+5orzM1FR7MTMpEBZ+Q89VCSH4ikPam5CyXTlxUwxT6ejo0PaMTIJFTAypdYJ54Md4otF5LmW7/MsWyxEoK6c7/pS8rhfCiH+JzOzFKVEND4d4su3B5USKIepPKg5CCWLdHVTy4tAtbW1oWkawcEJqICBoPqO5oPtQcWmZLp52YX4Mkl+UAkTCpi+u9NMDb/fn1dbKt3yoptwWkxMTOR0XHUxEorJ0Fq+PCi3282SJUtwReWlaSA4jGWpy8i5sAUqkhSoUvGg5iVQQnK9EKJq1v435MYsRSmRniSRy/k086Gpoh4A0ysfqzDfTIK2QOXJgwK5DuUwdVyak1gilhJNxZmJJW8CIyEZoi0bgRJCfAz4FfAJYJ8Q4i1pT381V4YpSofRsMwMcyT0vAtUa6WcbxRxxrCwlEClYVkWExGZNacn9LyFY+11KLcpVyACkYm82FFM2GH0qUkp5qUiUPNZg/oQsNkwjKAQYgXwCyHECsMwvkUWJ4oJIW4EvgGsAQaAfzAM43YhhBv4LvA2ZL3VtwzD+Hq2PleRW0zL5OjoCQC8YWfeBarKU4nL4SKWiGFpKpMvncHQCOF4BEdcx6058Xg8535RDrAFiqgFHhgPT7K0aklebCkW7E4SVszE660oiSJdmF+Iz2kYRhDAMIwu4FrgZiFE1gRKCNEB3AN8DagF3gl8XQhxM/AVQACrgMuB9woh3pONz1XknmA0xFQsjJ7QcCUceL3evNqjaRoehzx5TU2F+NLpDvQA4I04qayozFvHj9raWioqKtCTy4PjyoM6J3azWN3Mf61hNpmPQPUJIS62HxiGMQHcCjQCG8/4qoXRCfy3YRi/NAzDNAzjBeBxYAvwXuBvDcMYNQzjGPBNpFenKALSRzd4PJ6cTd5cCC6HDBxYuqXGbqQRCEshcMUceVt/gumCXUdCn2GX4syEkvOgHCUmUPMJ8b0HmNFV0zCMOPAeIcTt2TDCMIyngKfsx0KIeuDVwB1AK7A/7fBXyJ4wKnKM3UutENafbNy6C0CF+GYxmeza4EhoVNTkT6BAhvmcwy8Cag1qPkx7UHp5CZRhGCfP8twz2TUHhBA1wH3AdmBHcnco7ZAQsnOFogiYFqj8Z/DZuBxSoBxuJ1PBKUKhUN7T3wsBu2uDw8xfx3mb9vZ2nDukBzWuPKizEkvEiJtxdLSCKIbPJhk1ixVC/D7wHWSj2BPALmCn/WcYRkaTxpKtkX6F9JjeBdhXtPQrmx9QDbqKhNRFr4A8KDvEV11bQzAYYGRkRAkUMJHm7eZboJYuXYrLdAAwmscO68WA7T05caJRWiG+TBcEvg08CrwR+BYQAN4O/A+yF9+CSTah3Z58j7cZhhE2DGMU2SVdpB26jpkhP0UBY6ctO8zCESg7xFdZK8v6VJhPMllAAuVyuWiqbgRgYFytE56NvQMHAXmOgfKgACqBLyWz+lIkU8IX3KNfCLEKuB/4gmEY35n19B3Al4UQe5Kf+1mkQCqKALsGyhkvHIGyQ3wTXlkzogRKMhmZDsfmW6AAOurb2Bk5zGBIZVqejX9+7ocAmFZpjdqAzAXqPuASYIZAGYYRRYb5FsrHkKM7vi6ESK9x+h7wV8A/AvuQHt+/Aj/I4DMUeWAkOb7BFXMUkEDJn/0LwX2scjcogUpih/icBeBBAXQ0LEU7CZOECMWm8LsK4/dTSJiWmdpOJLeVQEkv5k4hxDHDMDIRpBkYhnEbcNtZDvlY8k9RZNjrBwXlQSVDfABhb1wJVJL0EF++m/oCNNQ34O5yEvHEGZgcprOuPd8mFRwz2kBZ4PF48lZgnQsyFahLgOXAC0KI3cCzyIy7HcA+wzASWbJPUeTYNSzOAkySgOlUc8uy8laYWghYlim914kAACAASURBVJUW4isMD6qurg5nXCfimTnpVzFNMDqd4KwVwEDQbJNpksTtwGHgg8CDyC4PXwdeAlROqCJFIdZBpXtQLo+beDzO+Hh5XwAjiSgxM45myqa+hZDVWF9fjzMuL1Eqk29ugtFpD6qtr7rkBCpTD6oRuN4wjCPpO5Mtiy45b6sUJYFlWalOEoUkUOmekrfSBwMxhoeHS2aGTibMvpFwOBx5tgi8Xi9ePAQIq0y+MxBMTkBe5mulImSVzCRdm0w9qN8hm7rOwDCMbsMw7js/kxSlQtQuILS0ghi1YeNzTsfoNZ+8EJf7OlShhfds6tzSI+gezah6peSxbyyclvwdF8LaYTbJVKB+BXxVCNGZRVsUJUbqrjxZn1EoAmVP1QXoRd6Zl7tAFVKRbjotFbIW6tR4X54tKUxS/RMTpSlQmYb4/h3ZyfwVIcSjpCVJGIYxkC3jFMVNILmwrcdlSC3fncxtltdOZ4MFEpMs1SqUQKXaHGlUVBWOQLXXLoWRXQxHRvNtSkFid3rXY/IcKzWBytSDqkWO3fhLYBB4B9Kr6hVCdGfHNEWxc2zsFACeiBOXy4XTmen9UHa5vG0T79z4ptTjmCtR9gI1WWA1UDZtDa1gQdiKEjdVcvBsUp3eI7IGqtQEKqMrRnLkxpPJPwCEEB5gE3DxmV6nKC9S84UKYFBhOpqm8ZYNr2NP/wH2DRwk5jIZGxsjkUgURHJAPiikNkfpNNQ34EzoxJ0m4+EJ6v21+TapoLAFypxKoFPGAiWEOAT8Fpkg8ahhGDPycg3DiADPJ/8UCiaS4xsKqQYqnaaKBgCGWkJUHnEzMjJCU1NTnq3KDyF7XEOBtDmyqaurwxGXAjUWHlcCNYvBkPT8zckYUBgF1tlkISG+W5FNWv8PcEwI8YwQ4stCiKuFEPmfQqcoOELJGo1CSjFPp94n08qDrjBx3SzrMF/ILgcwC+siV11dnUoA6BtXy9vpWJZF/6RM8tGnZIPdUhn1bjNvD8owjIPAQeB7QggHcBVwI7JP3hohxFMkPSzDMA7nwlhFcRFMtmFxmIWTYp6OZaVt6+U9vNBumVNIc7tAhmNr9UomiXCo/yhXd16Wb5MKhmBM9ij0ONw4EhqV1ZUl1w0lI8/HMIyEYRhPG4bxZcMwrkJ2krgDuAh4KJsGKooX+65cL1AP6ubVr0ltm2U+Xde+mdBNvWCyLW2aPTIUe2z0jLNTy5KRkOyuUeOqQkMrKM83WyxkDep1wFOGYQRnP2cYRgC4N/mnUACF70HV+2vpqG6le7xXeVBp31WhNRttqWyC0EGGp1SqeTr2KBu/wwskyluggAeAuBDiReSwwkeBZ5PJEQrFadhtWAp1DQqmZ0NZGgwNlW87HVug9IRWcB5UW10rhCAQU20+0xlNjrLx4QFCZS9QjcAW5NrTNcCnAV0IsY1pwdquOpkrAEzTTI2i1gvUg4JpgdJdDkITIUKhUEE0Sl1sUgktpl5wHlR7UxuchCkijE4FqPOVb8/EdGyBciWcJCi9FHNYwBqUYRijhmHcbxjGFwzDuA6oAV6DHNF+AfBfwKgQ4oHcmKooJkJxecFz4UCjcAXKnRy9UVXm49/tcKzP6S24hfbG+gYqg1I0n+vekWdrCoeUQCU7vpe1QM0m6SmdBHqBAeBE8v1WZsc0RTFj35E7TSkAhSpQ9uiNymopUOUY5oubCaKJKFjgcxdWeA+gtraWipBMn+6bGMyzNYXDSFgmSWgRmY5aigK1oE4SQohKZIujG5Ap5uuAbuAxZH++PzIMQ7UdVsxIkIACFqhkiK+iqoIRylOg0hMkfN7C+55cLhfVjgr6maAnoJrG2oxNyV4JZkiuqpS1QCXrnK4ARoHHgW8jO0qomifFaQSTrXP0RHEIlK9SrjuVpUCllQMUWoKETVNFA4foYzA4km9TCgZ7iGN8IgqUuUAhEyROAj9CJkQ8ZxhGLCdWKYoeu3UOMRl+KFSBcuvyFPBWyAtzWQpUAaeY2zRWN0B0OnRczoRiU/RNDDCSTDOPT0bRoKBaVGWLhQjUSmRo73rgI0CFEOJp4BGkYO00DMM6y+sVZUQ0Ie/qtAQ4HI6C6WQ+G4cu2+g83buDGk1jdHSUeDxesPbmgvG0YYXeqgL1oOoaoR/CCVXV8oXf/T2nJmSo0+/0oZng9/tLstHxQrL4jhmG8UPDMN5pGMYSpEf1G2Qm32PAkBDiHiHER3Nkq6KIiCbiAGjJSbqFlhlmo2vyFOge76G2thbLshgdLa+C0ImIbOrrSBReirnNknrZxDdqxbCs8r4PtsUJoMotvaZSDO/B+WXx7TYM45+ANyK9qruAm4DvZMk2RRETS8jor2YVbngP4C0bXpfadjVKO8stzGcPvXPGC3gNqqEJzQQLi2hCrSzY1Dpl9mmpCtSC4xhCiHbgVcCVyf9eCsSRU3W/hkygUJQ59kVEtwq3Bgqg3jc9vuExx06E3lyGAlX4HlRdXR26qZPQTULREB5naXXtzpQlrgaGOakESghxLzKLrxWYAJ4Bfg18FjnqXXWQUKSImUkPqoC7SMzFZGWkbAXKWcBZfD6fD6elk8BkeGKUOjUXCoB1rk6eUQIFgBv4Z6SHtNMwDDMnFilKgmgqxFf4AvWNGz/Hlx75JjEzTtxhlqFAJUN8BexBaZqGW3MRIU7/8ACrl6zIt0kFQSxUuinmsDCBerdhGGMLeXMhRJ1hGOW14qwAptegCj3EB7CyfjlvWn8zv9j3AGZSoCzLKtjEjmyTCvEV8BoUyDZME0wxGCjPdlRzMTkpv7tSFaiFJEk8IYT4CyHEOTs1CiEahRBfBJ7M3DRFMRNLZfEVdpKETaU72SDWrRONRlMnfjmQ7kEVskD53fJ3NDKuinVtSl2gFlqo+zXgpBDiGeBhYB8wBGhAE7AJmXb+auDHydcoypBoka1BVSbTdZ1+2VliaGiIqqqqfJq0KAwGh+mdkKPUCzlJAqDSUwFRGJ0M5NuUguD9l/4Bhx5+GShdgVpIHdSkYRifAtYAzwF/BDwI7ABeBO4D3gpsA9YZhvFxwzDGs2+yohgophAfTAtUr3OEgYZJHj36TFmkM+8fOJTadhTgLKh0avzVAASC5S1Q7mR7rmtXXJXyoEr1ZmrBaeaGYfQBXwG+IoTQgQbANAxDBYYVKWKzCnULnZV1HantwaZJfjP0DL59lfzRRW/Oo1W5J2HJ5Nu6CT8ahS1QtRVSoCbCpw31Litipjy3rLhJNBrF4XAUtOd7PpxXP5dkJp/qf684jUiyJY1eJGtQtXMMwdvXb+TBksUlbiarQ+IyKcTlcuXXoLNQWylTy6cSYaLRKG53+dVCJcxEKoFnKiT7ElZWVpZsQk/GnSQUijNhWRb7B2WTe0e8cMe9n5MSPenTSSQFSkM2ii3kC12Dvw6AuDNRdu2obOwbCqfuLPkECVACpcgBTx7bTiQuPShn3FE0XZYd2qzToQx6vtkhPiwKOrwH0FzRAEDUVb4CZRfAu9IEqlTXn0AJlCIH3L3v/tR2pdNf0GGjdD639WMzHpuJ0q9Ft+/INauw159AzoQCiLkSjIyUZ6r5tAflSAlUsdwAZkJGAiWE+DshxGlXHSHESiHEY+dvlqKYafTXp7ZrqqrzaMnC2NSygVvWXJd6nCgDgUqF+CwKfqG9xluFA52E02JgpDyXvu3MUpfDpUJ8Z+GdwE4hxCZ7hxDi08DLnGfihaL4cTnkT6B5sLLowg92MShAIlH67SXtEJ9G4Yf4dE2n1iOTWXrG+vNsTX4IJweB+p1eJiZkgXUpC1SmYnIhcuT7diHEPwLXAWuBTxqG8cNsGacoTkanZPlbZdBDZVtxnTx+lz+1nYjH82jJ4pAe4it0DwqguaKe4cgoA8Hy6pdoY0+q9rl8TI6U/hpURgJlGMa4EOL9ydd/Hjlu41bDMH53vgYJIa4A7jcMozn52IPsnh5NO+xZwzBuOt/PUmSfyWiQ7vEedDTcUUfRnTwVrjLzoIooxAfQXNXEgZEj7K/t5oWTu7m8fdO5X1RCTMVlarnP5S2LEF9GAiWEuBT4AbKrxMeAS4D7hRDfA75kGMaCK+mEEBrwAeCbs57aCIwYhtGSia2KxeXYaDeWZdGo1+Ew9aI7eZorG1PbiXj5CBRFkCQBUJ82ZuMfnvkBP/+D7+fRmsVnKuVBeZmclJGKYrsJXAiZrkE9D/QBFxqG8X3DMP4UeD3wJuBAhu/5FeAjyH5/6WwGXsrwPRWLzFBIpv96TZlDU2wnT3v19H1QvBxCfNa0B1UMAlXrLZ6km1yQCvE5vQSD0g9QWXyn8z7DMN5oGMYpe4dhGI8ivZ17MnzPHxiGsRnZ1y+dS4FmIcQeIUS/EOJuIURbhp+hyDHDSYFyRuVPq9gEqsZbjUNzAFKgrBKvhUqYMlPRLtQtdGp9MwXKtEo/0zId24NyWg4sy8Lv9+NwOPJsVe7ISKAMw/ivM+wPGYbx6Qzfs+cMTwWR03uvBwQwBfwyk89Q5J7hqeTIsCl54Sg2gQL40rWfAMDEZGpqKs/W5Jb0Nahi8KBaK5tnPLYv2OVCMCa9JoclL93FFkJfKJmuQQ0CZ7y1tBMcsoFhGLfN+uzbgEEhRIdhGN3Z+hxFdhgPy9TXRDAGeIryBHIlu0VbGgQCAfx+/zleUbxMh/iKYw2qvbp1xuPJaJAKd+l+P7MZS55fniINoS+UTNPMPzvH+6wC3ofM6ssaQoivAj8zDMNe27I7RJbXrVOREEgOv9Nj8oJXLF0k0nHp0mZTswgEArS2tp7jFcVLsWXxOR1Obll+LQ8efxyAiUiQJZVN+TVqERkLy8QIR1z2TCzGG8CFkGma+Y/n2i+EeAH4JHJYYba4CLhMCPFHycffBh4wDKM8S8kLnPTprFU1xXl3ZxcaW0mBKmXSm8UWgwcF8IcXv4mn9j7HRFWEnvF+Vjd05tukRWNsSv4e9WTRTakLVLZ78e0GXpXl9/wAMAocBo4h66H+OMufocgSdojPEdeLNvyQCvHppS9Q8bRmscXgQYFcK6vR5IX5UP/RPFuzuNgRCmtKfm+lLlCZrkFtmGN3DfBFpJBkjGEYjwO1aY+HgXedz3sqFod4Ik4wNoWGhsPUivbkceu2BwXj46U9FDpRRM1i01lWvZST5jAv9OzmA7wz3+YsGqGoTNqJhaQLVaw3gfMl0zWovcgkidnDY7qR61CKMmQ8KivbfboHDa1oTx7bgzLLIMQXT04+dmg6TmfxtNG8tOVCtnXvZYQAY+HxsqiPSpgJpuJhNDTCk9PDCkuZTH+RK2Y9tpCht37DMEq7cERxRsbDUqDcFHeGkUsvnzWocHJul8dZ2MMKZ9Pe1o73sJOQP8aJsVPUtpS+QNkp9X6Xl+CkTDcv1nNsvsxboM4Q1ptNvRACwzD2n4dNiiLFTpBwmbJwsFjv7px2koQO4xPjJBKJki2GjMRkqMjrKq7x6a2trXgiUqCOjXZzUcv6fJuUc4KxEAB+t78sOpnDwjyoM4X1bOznLKA0z2bFWbEFyhGTP5FivbvTNR2n7iRuxlPrUHV1dfk2KydEE0mBchfP+hOAz+ejXq9mlCkODhyF0tcngsn1J5/DSyKRwO1243YX143FQlmIQH0EeBYo7VVjRcYEkhl8RGSUt1gFCmSquRQoq8QFSg7AS5+DVSwsq2njCP0cHz2Zb1MWhVDSg/LoLiBR8t4TLEyg/gkQhmF0CyESQIuqRVKkMx6Ra1BmWGaGFbVA6U6mKP11qJgpBcrnKS4PCmBtyyoe69vJYGSUuJnAqZd24CYYkx6UK3nZLubza74sRKB6gR8IIZ5HhvL+XAgxOdeBhmF8NRvGKYoLu0bDEZcpy8WUFTab6Uw+SlygZBaf31N87YKWty3DfdxB1JNgb7/Bxa3zWSYvXuwQn9NykKD0159gYQL1XuCvgFuR60w3IgcVzsYClECVIbYH5SziIl0bt14exbrxIhaopUuXUj3hZcgTZHff/pIXKDvE50hoSqBmYxjG08BNAEKILuCGZBGtQgFAMJrstGwWbw2Ujd2ANKGbJStQCTOBiQUW+L3Ftwbl9/upd1QzRJBDA0d5+vjzXNWxGUeJhvomo1KgbLdACdQZMAxjdh2UQpGqqdHN4vegqjxyCFzCUboCFUsmSOhF1kUinfa6pRykl4NjXRzc1kU0EeO1K7fk26ycYHeRIFq8o2wWSrZ78SnKmGmBKt42RzZVbml/wiFDfKU4uNDO4NPM4pgFNReiZRXO+PRl7OR4Xx6tyS2hZJJEIiJdqGI/x+aDEihF1ojEZU2NXgIhviqPPPktj0Y0GiUSieTZouyzp19OsHGYetEKVPvSdpadnC4BKOWWR9FkxmV0qjz68IESKEUWiSQ9KK0EBKo6KVCaX65nlGKY7+BQFwDVE96i6WQ+m9bWVnxhF0tGpTDZSR+liB2SjU7J80x5UGdACPGoEKJ2jv1NQogd52+WohgppRDfirplAAS98t80MjKST3NywmBI5jj5plxF60FVVlZSVVWFmZC1d6UtUPLflojE0XUdn6/4ElsWykJ68V0L2HmcrwH+dI46qPXIybqKMiOeiJOwTDQL9CLuZG6zJjkEb1KbAirp7+9n/frS6qczGJSi64o5itaDAmhqauLoWC8wfREvReyias2SN4DF1Nw3UxaSxTeMHPWuJf/+DEikPW8Bk8BnsmadomgIp4X3oPjj436XD7fDRTQRI6GZ9Pf359ukrDM6NQbIurVi9aAA6urq0Ebl7y5Wwh5UKqnFKv7za74spA7qZWAlgBDiMeCthmGM5sowRXERTkyH94q9iwSApmnUeqsZCA4Td5aeQCXMhKyrscCZ0Ivag6qvr0c7IrfjpexBJf9tulX8IfT5kmkd1HVz7RdCuIHNhmE8d15WKYqOUsrgs6n11jAQHMZyw+joKJFIpKgv5OlMRINYWDgSGm6Xu6hvKOrr69Gt0vegYqmyACVQZ0UIcSVwO3JNanaihZXp+yqKl1SChKWVTOfvBn8dDIOn3g/BKAMDA3R0dOTbrKwwnuw870wUf1F1fX09WhkIVHTWGlQ5kGma+beBQeAPgSng3cDnkWtQ78yOaYpiIpKWwVdbe1qCZ1GyrGYpAGa1PE1KKcyXmt2V0Iv+YldXV4eWrKO2vYxSZLrzR/msQWUqUBcBtxmGcQ+wCznq/e+BTwKfypZxiuIhnBbiKxUPqrNOeksBh+wx2NdXOl0KSqmxr8vlwu+VvROnIlN5tiZ32GtQyoM6N3GmBxceBC5Obj8GXHC+RimKj/QaqFIRqA1Na3BoOn3RIRK6ycDAQL5Nyhr2cElHwkFFRUWerTl/aiploe5UJJxnS3JHKsRXQuu85yJTgdoOfFQIoQO7gdcn918IRLNhmKK4iJTgGpTP5aW9ZikWFhF3nP7+/pLpyZfyoBKlcbGrqaoBIBwtTYEyTZOEmQBL1viUwnc2HzIVqM8D/we4DbgD2CCEOAr8HPjvLNmmKCKm66D0klmDgrR1qBqdaDTK2NhYni3KDvZwSWfcURLhorpq+ZsLx0qvZyLAVFwKr25qOPTS8HrnQ0YCZRjGC0An8ONkLdRm4JvA+4FPZ806RdEQCMqIr8fhxu1259ma7LFxyToAxmrDWFglsw5ld8YuhbZUAA219cB0uUOpMRWbFqiqqip0vTzaqGb8rzQMIwg0CSHeAkwADwE/NwyjNGIgigVhC1RFEU5mPRuvar+EKncFAW2SsDdeMpl8qYywElnPaK5vBiBqlqhAJT0oh6lRXV26Hdtnk2mz2GohxIPAXuBuYAnwT8BLQoj2LNqnKBLGQzJkVOUrrdCDz+XlgmYBQNRVegJVKhlhSxqkQMVIlMw6YTrTHpSuBGoe/CPgAdqRdVAAnwACwD9nwS5FkTE5JVOxq/zFfzc+m+bKRgCi7kTJCJQdCnOg4/cXv9dbk/zdmZrJ+Pj4OY4uPkJpIT4lUOfmVuDPDcPosXcYhnEC+Djw2mwYpiguQtEQALWVNXm2JPvYiRKTldFUy6Nix04m8Ht8JdEV2+VwoVlg6TA4NJhvc7JOOK4EaiFUMu05peM4j/dUFDFTyfTeuqrSyeCzuaJtEw7dQcgbLZl6KLssoLJEQrKapuHUZIe1vuHS8HLTsZNa1BrU/HgY+GshhCv52BJCNCEz+X6XFcsURYWdZm6n+5YSXpdXelEahL2xkgjzReNyDaqqonRCsh5dZo8Ojgzl2ZLsMxaWYUtH3KEEah58HOhAzojyA/8LnABqkO2OSoYTY6f47rb/5GSgN9+mFCyxWIyYJduw1FfX59ma3GAnSoxVh+ntLf7fQjQh16BKac3Q55IzrQYDw3m2JPuMhGT9nSteXkkSmY7b6AeuFkJch+xo7gQOAL8rtTTzl/tf4cnj23ny+HZcDhd/f9P/pa26Jd9mFRRjY2OYyW6d9kWi1HhN56u43/hfgv4op06dyrc5543d1626snQEqtZbzWBkhMGJ0hOo4ZAcvecqkcLq+bKQke8b5tjdn/yzWS+EwDCM/edtWYFw4+qtPHL0GU6O9xJLxPjFvgf45FUfyLdZBcULx18i4pUXPK+zdIp00+moXorX6SFMhMMDJ4hGo0VdkBy35DBsu4ddKdBYWc+hwDFGQ2NYllUSyR8Ae/sNdvS+DECNu3yKdGFhIb69wMvJ/+5Ne5z+Z+8rGeKRGFsqLk49HgqpIcLpxM0E/3nkXgCqtAqaKxrzbFFu0HWdt26QLSf7mseLPsxnC1RtCSW1NFQmu0loUSYnJ/NsTfb46uPTlTstvtI8v87EQgRqBXLk+4q07SnguuT2yrT9JcOOHTvY+9gurvRtBMAYOsLTx5/Ps1WFg51dBHBz45aSvrt7o7gRF06i7gSvHD+Yb3MyxjRNLM0CC2qqSseDqvfJEoeYy2RkZCTP1mSHaFrrJlfUQV11aTRini/zDvEZhnF89j4hhAWcnOu5UmH16tU8+uijRPYFuPiqC3ipfx/37HuIq5ddhq6V7sV4vtj1T86YzoUtIs/W5BZd11lR2c7ByWM83fciN3Njvk3KiEgyQUK3SqPNkU1LZRMgO36MjIywfPnyPFt0/gyEptfTVpyop/qy0rmhmA/qCnsOWltbWbVqFdFolKu0C2nw1XFqoo/nT76Ub9MKAtuDcib0khmzcTbeJG4C4Eji5Iy722IinOxKoJVIo1iblirZ7ijiTpSMBzUwKVPmlzqacJVZijkUoEAJIa4QQgykPXYLIf5VCDEihBgUQnx+sW3asmULAC8+/wI3r9oKwL/t+FnRXqAWSig2NSOUl85k0oPSE6U1ZuNMbF65CV/ETVw3efzwc/k2JyPGJgMAOCwdpzOjRN6CZElFIxoaMVeCoeHSqIXqTwqUJyG/JyVQeUIIoQkh/gT4LZCeHvUVQACrgMuB9woh3rOYtnV2dtLW1kYoFKJ5ooalVUuYiEymMmtKnffdexvvu/c2TMs87bnR5MXOpTnw+XyLbdqio+s6q/U2AH57+Mk8W5MZIxPJmhqtdMQJZLujOk8NaHAqUBpjUfqDUqCcYZmRWFNTeq3EzsZC0sw/eobXv1sIMeN2xTCMf8nAlq8ge/x9Dfhi2v73Au9Lzp0aFUJ8E/gQ8JMMPiMjNE1jy5Yt/PznP+f5bdu5/nXXcMfue3iiaxtXdWxeLDPygmlOi1IoOkWlZ2ZrnIEx2ffM7yx9cbLZ3LyRfUPHOBHsoX9ykCXJtY9iIWDfVOilJVAAbdVLGBkcoz84VBKp5gNBuQZlBeV5qDyoM/Pnc/z1ISfrpu/7bIa2/MAwjM3Ai/YOIUQt0Aqk11W9AmzM8DMyZt26dTQ2NhIIBGgIVuLSnezs3cs9+x5cbFMWlbgZT22PJ6ew2jzXvYO7j8h/f4On9MN7Nis6OqkMSif/nv0P5dmahROYnB4uWWq01bQCEHJECAaDebbm/LHXoMzxGJpWWmuG82EhWXwrcmlIemf0NOxvI5S2L4Rsr7So2F7Ur371K3Y8+wLvvPFN/GT3Pdy199esqFvGJa0XFP3d2lzY9TIA45FJlqY998LJ3ant1TWdi2dUnmlra6N6wsd4dYQnj23nrRten8ogKwbs2V2eEiyqbk0mSkRdcYaHh4v6gm5ZVirE5445ymqSrk2h/2vtW6D0+JEfyEsV3saNG2loaGBkZIQlEzW8/YJbAfjGU9/jw/d9nolI6RQH2sTNmQKVzsSUfFwz7uOWVxVnynUmVFVV0WY1UjnpxrRM7nr5vnybtCDs760U21K1llAmXzAaIhKP4HV4cJTZoEKbghao5LpTHzJJwmYdM0N+i4bD4eD6668H4PHHH+cNq69PPTcaDrC9BFPP00N8Y+HAjOdODcluClc0biyLDD4bTdNob2+neVDWEO3pO5BK3S4GJsJSoCo8xT+ocDYpD8otPahiZjwqvye/Q95IKIEqTO4AviyEaBRCdCLXuO7IlzHr1q2jvb2dUCjEtue28e5Nb0k9968v/pTJSPHHvdNJ96D2HN6XSpoYHR1NrUldc9mWvNiWT5YuXYo34qRer2YiGuSHO+4smlHj41H5vTX4S69urcnfgEPTibtMBkeKe3ChHZFxI6calVJR9XwpBoH6K2SPv33AC8A9wA/yZYymadx4owxnPffcc1y79Eq++trPpJ7/7vb/LJoL1XxI96BeGH2ZO372X4TDYZ548kliTilenS3L8mVe3mhra0NDY81YKx6HmyePb+eZEy+e+4UFwERcLuk2VZVeXzdd12lO9qs7EZhrWbt4sAXKaTqA8ksxhwzHbeQSwzAeB2rTHoeBjyX/CoJly5aR7NrOE088wRve8AZEw0qM4aPs7N3Lrt69XLp00RMNc0IizYOydHhh+GXG/y1A79Qg1nKo8VSV5FrGuejo6MDj8TDZE+BNl93Izw8+wB0v3cPaxpU0VzTk27yzEkKGI1trl+TZktzQJOrHNwAAIABJREFUWddOb2iAgfBwUaea22u+jri0X4X4FPPmhhtuQNM0du7cySuvvMLf3PDn3LLmOgAODB7Os3XZw54bZBOuk404exvletSy2qVzvazkcblcXHDBBQD4BnSqPZWMhgN88+kfzFnQXCgMT4wS9EbAgtVNnfk2Jyd01Mnf5JSjuLua210kdDmsWgmUYv40NjamEibuvfdeenp6WFXfCUxXf5cC3ae65UYyallRW8n6jRuY8suR4a9LinI5cskllwBw4OX9fHHrJwA4NnYSY+hIPs06Ky/3HMDSoDrmK8k1KCA18iXqKu5MvmNjJwFwJDVWCZRiQVx99dVcfPHFxONxfvazn1GBDHVt697Jj3f9Is/WZYdXDhoANLnq0DSN/uAQ8dUuTM1CNK7i8rZNebYwf7S1tdHY2EgwGCQyGOLN628G4Ec77irYrL6Dg1I86yndi50dYrVroYqVY6PJm8OxeFkW6YISqPNC0zTe8IY30NnZyeTkJM899DRLvPLu7YGDj/A/B36TZwvPj0gkQtfxLgDqq+pormjEtMzUv+vWta/Np3l5R9M0Lr5YDrPctWsXbxA30OCv43jgFP+56+6CTJY5OCS/z/aK1jxbkjs6amSIL+KNMzBcnJl8gfA4o+EAXocHd0yOeXc4HPk2a9FRAnWeOBwO3vGOd1BfX8/QwCA1L08vyN7/yiMzetkVG/v370+lmXs9XpbXtqWeEw0rubLj0nyZVjBs2rQJTdM4dOgQjrjGZ67+U1y6k0e7nuWXBx7Ot3mn0TclL9hXrCnd767C7afZ04ClwZGR4hxVZ68/1btr0NBobm7Os0X5QQlUFvD5fLz//e/nuuuuo6OpjfWGzI4aj06wvWtnnq3LnJdeeklOXgWcuoMbV7069dzr1l6bJ6sKi8rKStasWYNpmuzZs4fVDZ186uo/AeDe/Q/RM144XbUHhgaI6nGw4NJ1pR2aXZNcD+6e6s2vIRlij7FxxOUlurW1dD3es1FwaebFSkVFBVu3bmXr1q2MjIzwDw98jyPuHv75hX/H4/Jw6bLCTzs/ceIEzzzzDMFgkHA4zPDwMI5aGVZw6A42tWzgn2/5a+KJOMvSvKly5+KLL+bgwYPs2rWLK6+8ksvbNnHRkvXs6T/A3z7xHb5y/Wdo9Nfn20x27pOdTryaG7er9PrwpbOxdT3P9O5g0BwrylTzyags+LciCcBRtgKlPKgcUF9fz8du/QAAlgbffvqHBCbG82zV2Tlw4AA/+clPOHjwIKdOnUotLi9f0QmAMzmaYWnVEiVOs1i7di1+v5/BwUEOHToEwGe3/Ckd1a0Mhkb49INfKYjhlnsPyw5htd7STZCw2dS2AYCgN8J4gZ97c2ELVCwofzdKoBRZpb1+KX+zVXaYmHJE+dzPvsqPf/JjHn/8cbq6uohG83PBGhkZ4fvf/z63334727ZtIxQKsWPHDu6++24SiQSbN2/m/e9/Px/96Ee57bbbuGCjrPVxauW3QDtfHA4H11xzDQC/+93vME0Tr8vLZ6/5MACRRJQvP/qtvCZNjI2N0RvoB6ClRAt002nw1+E13ZgOi33dRr7NWTATyZZpZiSOx+Mpq16X6agQXw4Rrav5/JUf5RvPfZ/hmiDPjeyh64kuNDQ0TaOlpYX29nZaWlpobm6mubkZt/vsoZeJiQn27t3L6OgogUCA8fFxqqqq6OjoYNmyZdTW1hKNRolGozgcDpYsWZIKbwwODvKTn/wkVbzY19eXuqACXHvttWzdunVGOCTUK0e9l2O3iIVw+eWX8/zzzzM0NMSuXbvYvHkzrVXNfOiyd3H7iz/lyOhxfnngYd664fWLZtMzJ15gRd0yllYt4cCBA4Q9snZteZl4wC3OBo6ZvezsfZmrL7gi3+YsiLGw9PoccZ3W1taiC1FmCyVQOeaS5Rv5gufj/O0T32GkPsRIfYhLgquInQzR29tLb+/MRVyv14vf78fv99PU1MSaNWtYuXIlsViMp59+mh07dhCPz+zu0NfXlwotzaapqYkrrriC5uZm7rrrLkKhEJ2dnWzevJndu3dz5MgRwp4Y/osauPhVl5x2IgSTi7UV7tLrfJ1NnE4nN9xwA7/4xS947LHHuPDCC/F4PFy/6hp8Lh/ffu7fufPl+7hn34N883VfSnXdzhXG0BG+/dyPAPj7m77AgQMHCPmkQK2oK4/eieurV3FsrJeDga58m7Igjo4c55GjTwPgiTppaWnJs0X5QwnUInBRy3oub9vE86fkInXvknG++e4vcurUKU6dOsXAwAADAwMMDg4SDocJh8OMjIxw8uRJdu3aha7r6LqeEqZ169alvKWqqipGR0c5ceIE3d3dhEIh3G43Ho+H8fFxBgcHeeCBB1K2rF69mne84x24XC4uvPBCJiYm+MTvvkxwYpgP/uoveYO4gfdc/Pup422BqlQCdU42bNhAW1sbp06d4tlnn+W662SXjauXbebkeA+/2PcgMTPOJx/8Mt+48XOsrF+eM1uGQ2Op7b994v/R0uMjuDqKQ9O5aMm6nH1uIbGueTUPjT3NUGwM0zLRtcJf0RgJjfH/t3fn8VFVZwPHfzOTyUJ2AmFJIJBAjgECymYEpKApUqxF2SwglVKLbbVi7ebSammltVLta219sa8t1aLSoiAg1bILKCJrgiwHEiSEgYRAQgjZl3n/uHeGSSABgWRmkuf7+fAh9965M+dwhzz3nHvOeR5f85x7O6gyoM0+fwIJUC1m9uBp5J47wcmSU+SdL+Clz/7Oj4fPpmfPC4mKnU4n5eXllJWVUVpaSm5uLocPHyY3N5eamhqUUowaNeqiO6r4+HhSUy8eJVhbW8v+/fvZtm0bDoeDlJQUJkyYQEDAhcseHh5OaXW5e/t9vZb7+t/jztzpOhZqlwB1ORaLhTFjxrBw4UK2bt3KoEGD3MvT3JMylvNVZXx4eCMA8z56mRe/9jSRzTRgodzjmhZXllAVXwEW6NdJERYU2iyf6WuSu/ciYJ+VGnstRwqP0Sumh7eL1KSyqnJ+9MFc93a3khjstW13BB9IgGoxEcHhvDRuLr9YO59DZ46w3ZHBM+tfZO5tj7nv7CwWi7t7r0OHDiQkJDBixAjKy8uprq7+0mtx2Ww2UlNTSU1NpaSkhLCwsCvqyy6qKHav03b8nNEFKV18V6Z79+6kpKRw4MABVq5cybRp07BYLNhtdmYNvJd7+93FE2ueI+98AW/seZcfpn27WcpRUlU/L1m52b13c/xNzfJ5vqh9+/Z0qIogz36WtQc20WtED28XqUmrszdRXmMskfXo0O+w5o1V2O12YmJ8e3X85uT7bd5W5mcjvsew7oMB4znByoNrqa6tbvKckJCQa14oMjw8vNHg5BpC7lJQagwxX5Sx1D0TPyKo7SVLu1pjx44lODiYrKwsduyonyMqNLAdj6TNAmBzzmc8t/kVKq9hCPq247vZd+rQRftdqRp6hsbX258W33pXkGjIYrEwIMboztyel+mTS0+5HCg4zFuZ7wEw86bJdHYa8+Y6d+7s7s1oi9puzb0kIjicOWmz6BubDMCbmcv42X9/69UUDUkNnoW8u/8/bDq6jRUH1wAQH9GF3j7ePeJLIiIi+PrXvw7A6tWrOX26/ur2vWJ6kBRt/JvvOrGXXSf3XtXnHCzI4oWP/8rcDX+sl/kYLiS7s+c5aV/YjgCLjadHzWkz3XsutyQPJaDaSkltKTtPZHq7OI3KyDvg/vm2nsPcg6fa8gAJkADlFRaLhSdHPkx6ojF3xlGSx/NbFnjtDs/VxfjAoG8SHBBERt4B/rztHwBM6XcXz495EptV5kF9GX379mXAgAHU1NSwdOlSamvrB5Bf3faYey3Dv+/8F5l5B+olh7ycmrpanl7/gnvbvfK1ydXFV15cyg2V3Xh9wh/p10YGR3hKSkykw1kjKH94aKN3C9OEHDO1xiNpswi2B7sDVFt+/gQSoLzGbrMze8h00s317Xad2Mvs5T+nuKLlZ727uhh7RnfnmdE/wm52+XWL6MI9KXcQYJNHlVdj7NixREZGcvLkSdatW1fvWFBAILMHT0N1SKK4soRnP/oTz21+5YpvUo40WAT1i4YBypUuvNbK8OHDsQfYr6Em/isoKIiU0CTASCR6ue50b6iqqeJzs5s2uUMigAQokwQoL5sxYAJD442UDcWVJXx3+c955bM3WvQ/kuuz7FY7Se0TeGHsL5k9eDq/HP2otJyuQXBwMPfcY4yI3Lp1Kzt37qx3PCwwlLmjH3MPSMnI23/Fq2+7FhN1ySk+Xm/7TEkRAOFBYe7Eim1V36QUgioCqHbW8PGxHZc/oYVtP5FBZU0lidHdiQ2NobKyksLCQmw2Gx07dvR28bxKApSXhdiD+fGw2cwaeK9738YvtrJSr22xMlTVGQEq0GwpdQ6PJT1pRJtYs625JSQkcOeddwKwatUqsrKy6h23Wq08M+pR93PAl7b+je2ODI4U5nDo9BGyC3Mu2fVXZg4jjzQHr+x07HWv91dRUcE5syU+bFBavWkFbVFSUhKRJcZKKL6Wo2111kfuCdWuEZZ5ecYK+LGxsW0yB5QnCVA+wGKxMLb3KJ4c+UP3vsV7V3Do9JEW+fzqWmMCsN3WNruBmtvAgQMZPnw4TqeTJUuWkJ+fX+945/BYfjL8QcKDwsgvPc38LQt4fM1z/GLdfJ5Y8xx/3/Wvi96zrNpoQQ3smkqHdu05U17Ewt1LqKqu4pl3f0+NtQ6r08KwIbe0SB19WZcuXYgv64C11sKJknzyz3svieHZinO8su0NPji0gcKys7yx5133MdczyT17jAn9Xbt29UoZfYkEKB9yY5c+vD35z3Q0UzO8mbmsRQZOuLv4JEA1m9tvv52+fftSVVXF22+/TVlZ/S66mHbR/Gnc3EuuebgmezOPrHravaoHQJmZUt5aAw/caLS+1x3Zwn1L5/BFgHEHPrTrjQQHyRqKVquV3km9CCsNAmD3yX1eK8vGL7ay8ehWFu7+N0v3f0BVbTVdwmN5etSjdAmPxeFwsGfPHqxWK7fcIjcXEqB8jM1qY176z7BZbRwoyGLJvlWXP+kaVdYaXUOBVglQzcVisTB+/Hji4uIoLi5m6dKlF2VbDg1sx//e9Vsev/UHPD/mSaamjsdmjrDMO1/AYx/8mvVHPgYupGPYuyuTlX9fRp/S+uvrRQaG82DafS1QM/+QlJRE+HkjQH2au8trI2ZPlpxy/7w6exMAc9Jm0a+Twul08sEHHwCQlpbWpifoukiA8kFRIZH8YMi3AHhn3yo2frH1us+TKqsq50hhDlP+9X0qaioJsAYQKC2oZmW325k8eTIhISFkZ2fz0UcfXfSadvYQBnZNpUd0N+7pM5a3p/zFvTZiUUUxC7YvYm32ZrblGt1AATXGOo2W3CoSjkW732femJ/L6h8ekpKSCCsNxFIH+wsOszf/oFfKkdegezG1k3KvyZiRkYHD4SAsLIyRI0d6o3g+p20/PfVht/YYyse5O9h1Yi+vfPYGr3z2BvPSf0bvmJ6XfP2lsobWOes4WJBFQlQ8GXn72XXic0YkDOXQmSO806Bl9s3Uu2Q4eQuIjIxk4sSJLFq0iE2bNhEfH0/v3r2bPOfrKp3UTjfw0//OA+CvO94CwFZroXdIArMf+i4Oh4Njx45hjw5mYMqNBAW07oy5X1Z4eDi9uiVxprCM0x1K2ZSzjf6dU1q0DE6nk+PFJwCYmjoeJ07GJBmBqLKykrVrjYFR6enpBAUFtWjZfJX8RvJhj93yAG9lvsd/Dm8A4Km1z/P25D9jtVipqq12/xIqrjjHL9bO51TpGaYPuJuRCTdT66zjD1teJbuo/rDlTTnbLvqcBwdP5/akEc1fIQEYd/OjR49mw4YNLF26lLvvvhulVJPnJETFs3jKX/jLttfZnPMZABHnQvjqbenY7XZ69OhBjx49WqD0/mvo0KHoZVmc7lDK1txdTOwzrtnTnnjanPMZJVWlhAeGcnfKHe4bSqfTyYcffkhpaSnx8fH079+/xcrk6yRA+bDAgEBmDpzCbYnD+cl/nwVg6pKH3cfjIjoTYLGRU+xw71uUsYxFGcsu+949o7sxe/D0i5Y5Ei3j1ltvJS8vjwMHDrB48WL69+/P2LFjCQkJqfe6kpIStm7dSk1NDSNGjOChm++n8kwZ2ceOcFPgDZdtfYkLlFLEtoshv6SEkvBKlh9czfeGtNxzuv3mZNwQe7A7ONXV1bFy5Ur27NlDQEAA48aNa7PJCS9FApQf6B4Vx8ybJvOP3Uvq7Xecy6u3HRMSzZnyIvd2QmQc3x44ha4RnTladJw+sb0JtNmprq2WEXteZrFYmDRpEtu2bWP9+vVkZmaSnZ1NSkoKcXFxxMbGkpmZWS9B5e7duxk8eDA1e0voXBnBmPu/Kr/MvgSr1cqQIUM4tekMJeGVrD/yMbcnDm+02/x6K6+pBOCbqeMBIzgtX76czMxMAgICmDp1aptfOaIhCVB+YlzybXQMjWH+lgUkRnenT2wyO09kUlBaSFRwBKN73sKkvnfym40vkXvuJA8Onkb/TikEmt2AN3bp434vCU6+wTWUODk5mRUrVnDs2DF27Nhx0QroKSkpWK1W9u3bx6effgpAYmKidOldhYEDB7Jx40bCS4IoCa/k1e1vMv+Op1ok0FeYqTRcUwlWrFhBZmYmdrudadOmyfW8BAlQfmRI3AD+fe//urc9M9+6/GLUIzidTlmiyI/ExMQwc+ZMd1Zkh8NBfn4+nTt3ZuTIke4VrYcNG8a6devIy8sjPT3dy6X2TyEhIfTv35+q3TvJVoUcK3aw7fhu9yTZ5lRuzl0LDgji6NGjZGRkYLfbmTFjBt26dWv2z/dHEqBaGavFCtLr43csFgsJCQkkJDT+TLBr167MmDGjBUvVOt18883s2rWLiLNBnI6q5sVP/o8HBk1lTK/mHdpdYXbxBduCWLvWSGUzYsQICU5NkHlQQog2JTY2lsTERDrmh5KIsZzQkn2r3AGkubieQZ3IdeBwOAgNDSUtLa1ZP9PfSYASQrQ5d955JyGBwQQfrCXGHkVxxTkeXPE4//78/WZLHlphdvFt/8SYJjBy5EgCA2W+WlMkQAkh2pz27dszfvx4LFiIzrLTMbg95dUVvLNvFWuzN1/XzzpZcoqn1vye4soSAIpPFxEdHc2gQYOu6+e0RhKghBBtUkpKCmlpaQSV2+iZHc2NnfoC8NrOxcx491H2nNx/zZ+x79Qh5vznGQ4XHgUgsqIdVqeV0aNHt/lUGldCBkkIIdqs9PR0HA4Hubm5dP8ihqDeN7HNsZvKmkp+u+llEiLj6BObzMS+44gICrui96ysqeLQmSMs+OyfFJQVuvfHVXcgLNdC586d6devX3NVqVWRACWEaLNsNhuTJk3itdde49ixY9wQmUJcytc4cPowBwqyyCl2kFPsYNfJz3lm1KMcK3YQFhiKxWLBZrERYLVRUVPJ2YpzBNoCOX7uJKsOreNM2YUJ87GhHYgoDCTwi1oiIyKZMmWKTLC+QhKghBBtWkREBFOnTmXhwoUc3HuA0R1Gc+/ox1iTvYktOdtxmEkOf/D+U1/qfWNCovlGcjqOLUdwOBxERkZx//33Ex0dffmTBeBHAUopNQt4FfAcC/qQ1vp1LxVJCNFKdOnShYkTJ7J48WI2bNhAQEAAXxmUxpheX6Gw/CzPbvwTx8+dJNBmp1tkVyxYKK0q4+T5C/mdEiLjqKitYnTPWxjbexS1FTW8+eab5OfnExkZycyZM4mKivJiLf2P3wQoYCDwgtb6cW8XRAjR+iilGDNmDKtXr2bNmjVs3LiRlJQUkpKSmNV9InWWOkKD2hEfH+9Oh+F0OjlfVUqovR1W64UxZ2fOnGHRokWcPXuWmJgY7rvvPglOV8GfAtQg4CVvF0II0XqlpaURFhbGzp07ycnJITMzk8zMzHqvsVgsxMXF0aNHDzp16kRUVBTWaAt1dXUUFRVx+vRp1q1bR1lZGXFxcUydOpXQ0FAv1ci/+UWAUkrZgP7ADKXUi0AZ8Brwe621d3I3CyFaHYvFQmpqKqmpqRQVFZGRkUFBQQFOp5Pa2lpKS0s5ceIEx48f5/jx402+V69evZg8ebJMxr0GfhGggI7ADuB1YAKQAiwHzgGveLFcQohWKjo6mlGjRl20v7KykpycHHJycigqKnL/sVqttG/fnujoaLp27crQoUNlrtM18osApbXOA77isWuPUuplYCISoIQQLSgoKIjk5GSSk5O9XZRWzy9WklBK9VVKzW2wOxCo8EZ5hBBCND+/aEEBZ4EfK6WOA38DbgIeAR5u8iwhhBB+yy9aUFprB/AN4EGM507vAr/RWr/j1YIJIYRoNv7SgkJrvR4Y7O1yCCGEaBl+0YISQgjR9kiAEkII4ZMkQAkhhPBJfvMM6hrYAPLy8rxdDiGEEA14/G6+aFZzWwhQXQCmT5/u7XIIIYRoXBcg23NHWwhQ24FbgZNArZfLIoQQoj4bRnDa3vCAxemUtVaFEEL4HhkkIYQQwidJgBJCCOGTJEAJIYTwSRKghBBC+CQJUEIIIXySBCghhBA+SQKUEEIInyQBSgghhE9qCytJXDWl1ABgAdAfOALM0lpfNNvZlymlZgGvApUeux8C3gb+DEzCWGHjRa317zzOmwL8FmOG90fATK31KfNYd4zMxmnAKeCHWuv/mMcswG+A2UAgsBD4qda6phmrWY9SaijwvtY61twOxAt1VUqNBl4CkoAMYIbWut5SLs3hEvUPAkqAKo+XfaK1HmMe9/v6K6W+CjwH9DbLOV9r/WpbuPZN1N3vr7u0oBphfrGXA/8CooB5wGqlVIRXC/blDQRe0FqHefx5HZgLKIwv0RDgfqXUtwCUUn0wvpwzgRjgMLDY4z0XA5nmse8Ci5VSieax2cAE83N7m+/9ZLPW0KSUsiilHgBWY/zHcWnxuiqlOgDvmZ8dBSwD/quUarb/c03UPxUobPAdcP2S8vv6K6W6YWTZftb8rKnA75RSd9DKr/1l6u73110CVONGAXat9f9orau11ouBfcC93i3WlzYI2HOJ/fcD87TWRVrro8AfgAfNY/cBK7XWW7TWFcATwHClVG+lVDJGZuOntdZVZqbjFcB3PN73f7TWx7XWBcCvPN63uc0Fvo/xn9WTN+o6AdintX7X/P7MB4KA269/td0aq39j3wFoHfXvAbyltV6mta4zezk2AsNp/de+qbr7/XWXANW4PsCBBvsOYtyV+AWllA2je3KGUuqEUipLKfW4Uioao1m/3+PlnnXr43lMa10G5JrH+wDHtNalV3KueayrUqr99atZoxZorQcBO1w7lFJReKeuDY8BaJr3+3NR/U0DgVilVKZSKl8ptUQpFWce8/v6a603a62/59o2P/9WYDet/Npfpu5+f90lQDUuDChrsK8MaOeFslytjhi/rF4HemL0w38f+KF53LN+nnVrqu6X+3dpeNz1c7P/u2mtT1xid1iDcrh+bu66tvj3p5H6A5QCH2PcxSqgHKPrBVpR/QGUUpEYd/vbgJ0NytWwDK257stpBdddBkk0rhQIabCvHXDeC2W5KlrrPOArHrv2KKVeBr5mbnvWz7NuTdXd0sSxS53r+mJ669/NdRfY0nUt5eL/lF75/mitH/PcVko9BhSYzy9aTf3NrqnlGHfw0z3K1uqvfcO6a63rAL+/7tKCatx+jLsOTzdwcfPVZyml+iql5jbYHQhUAHnUr59n3erVXSnVDuhu7t8PdFdKhVzJueaxk1rrs9dWm6ujtS7CO3X1me+PUurXSqkUj12uARQVtJL6K6VGYrQc3gMmaa0r2sq1v1Tdzf1+f92lBdW4DYBFKfUjjGGqEzGe5yxr8izfchb4sVLqOMaInZuAR4CHMQZ8PKOUysRolv8EY2gowFvAFqXUKGAr8Dtgt9b6EIBSKgOYp5R6AhgGjAduMc/9J/ATpdQ6jLupX5n7vOmftHxdlwHPm0N5lwFzgDqMB9gtrT8wWCk1zdx+CViltS5QSvl9/ZVSScD7wFNa65cbHG7V1/4ydff76y4tqEZoraswusImAoXAU8Dd5qgVv6C1dgDfwBhhcw5jOOpvtNbvAE8Dn2MEqu3msQXmeXuBWeb2aaAvMNnjrScCKRjzI14DvqO1/tw8tgBYAnyCMXR1v/lZ3tTidTXnk9yFMTqqEOP5313m96qlfQcoArKAoxjzYmaY5WwN9X8ICMcYXn3e48/vaf3Xvqm6+/11l4y6QgghfJK0oIQQQvgkCVBCCCF8kgQoIYQQPkkClBBCCJ8kAUoIIYRPkgAlhBDCJ8lEXSGuE6XUPzBWem7MXIwJixuAcK11iyx7ZC4a/DHwLddEzEZeZwU+xcjfo1uibEI0RVpQQlw/czBWz+6Cka4FYKjHvj9gTG7swoU1AlvCI0BGU8EJwFy/7deYE1mF8DaZqCtEM1BK9QP2Aj3NPETeKkcwcAy4zWMlgMudk42xcsDG5iybEJcjXXxCtCBz7TN3F59SyomRBfUJjEU2d2Akk/spxrI054AntNb/NM8PB17AWELGCawH5jSRauObwFnP4KSU+iVGVtSOGDnPntRaf+BxjmsNtY3XocpCXDXp4hPC+54DHgXSMFaU3oURmIYAS4FXlVKuvFZ/xQhkd2CkUnFipNRu7GbzTuBD14ZS6h7zs+7DWGV6FbBEKRXhcc6HQHoT7ylEi5AAJYT3/UVrvUFrvQdjZerzGK0aDbyIkXunp1IqEaNFNE1rvd1sFc3ASPs9tpH3HoyxUKpLD6ASyDG7Hn+Nkaa72uM1+zFW/r7hutROiKskd0hCeF+Wx89lwFGttevhcIX5dxCQYP6slaqXcqcdRqvq/Uu8dyeM1apdFmGMNDyilNqJkYF1oda63OM1Z8y/Y79kPYS4rqQFJYT3VTfYrmvkdQHma28cdKEbAAABSklEQVQCbvT4kwwsbOScOowMqQCY6WIGYbS4PgFmApnmoA4X1++F2iuugRDNQAKUEP7jAGAHQrXWWVrrLOAkMB8jSF1KHsZgCACUUhOAB7XWq7XWczBaXiXAOI9zOnqcK4TXSBefEH5Ca62VUiuAN5RSDwEFwDyMwRUHGzltJzDAY9sGzFdK5WOMGEwDOps/uwzgQqI7IbxGWlBC+Jf7MYLJexgZYiOBr2qtzzby+lUYo/0A0FovAZ7BaHUdAp4FHtZar/c4ZyTwodZauviEV8lEXSFaMaVUO4x032O11ruu4PVWIAdjpODmZi6eEE2SFpQQrZjWugyjtfTQFZ4yHjgiwUn4AglQQrR+fwT6qwZj0xsyW09PAd9rkVIJcRnSxSeEEMInSQtKCCGET5IAJYQQwidJgBJCCOGTJEAJIYTwSRKghBBC+KT/B87xzMkIJb3xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_Q_in(frame, data):\n",
    "    plot(frame.Q_in, color='gray')\n",
    "    plot(data.Q_in, color='C2')\n",
    "    decorate(xlabel='Time (s)',\n",
    "             ylabel='Heat flux (W/$m^2$)')\n",
    "    \n",
    "plot_Q_in(Q_frame, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_Q_out(frame, data):\n",
    "    plot(frame.Q_out, color='gray')\n",
    "    plot(data.Q_out, color='C0')\n",
    "    decorate(xlabel='Time (s)',\n",
    "             ylabel='Heat flux (W/$m^2$)')\n",
    "    \n",
    "plot_Q_out(Q_frame, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results are also similar to what's in the paper (the bottom row):\n",
    "\n",
    "![Figure 3](https://ars.els-cdn.com/content/image/1-s2.0-S0378778816313056-gr3.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_error(frame, data):\n",
    "    model_Q_in = interpolate(Q_frame.Q_in)(data.index)\n",
    "    error_Q_in = model_Q_in - data.Q_in\n",
    "    \n",
    "    model_Q_out = interpolate(Q_frame.Q_out)(data.index)\n",
    "    error_Q_out = model_Q_out - data.Q_out\n",
    "    \n",
    "    return np.hstack([error_Q_in, error_Q_out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.33610526, -0.37832695, -0.39254863, ...,  2.26392988,\n",
       "       -0.7752054 ,  1.99865932])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors = compute_error(Q_frame, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the root mean squared error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17995317290043\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(np.mean(errors**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see if we can do any better than the parameters in the paper.\n",
    "\n",
    "In order to work with `leastsq`, we need a version of `params` with no units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.076, 0.272, 0.078, 212900, 113100]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = [0.076, 0.272, 0.078, 212900, 113100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an error function that takes a hypothetical set of parameters, runs the simulation, and returns an array of errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_func(params, data):\n",
    "    \"\"\"Run a simulation and return an array of errors.\n",
    "    \n",
    "    params: Params object or array\n",
    "    data: DataFrame\n",
    "    \n",
    "    returns: array of float\n",
    "    \"\"\"\n",
    "    print(params)\n",
    "    system = make_system(params, data)\n",
    "    system = remove_units(system)\n",
    "    \n",
    "    results, details = run_ode_solver(system, slope_func)\n",
    "    Q_frame = recompute_fluxes(results, system)\n",
    "    errors = compute_error(Q_frame, data)\n",
    "    print('RMSE', np.sqrt(np.mean(errors**2)))\n",
    "    return errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing `error_func`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.076, 0.272, 0.078, 212900, 113100]\n",
      "RMSE 2.17995317290043\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.33610526, -0.37832695, -0.39254863, ...,  2.26392988,\n",
       "       -0.7752054 ,  1.99865932])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors = error_func(params, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass `error_func` to `leastsq` to see if it can do any better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.600e-02 2.720e-01 7.800e-02 2.129e+05 1.131e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.60000011e-02 2.72000000e-01 7.80000000e-02 2.12900000e+05\n",
      " 1.13100000e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.60000000e-02 2.72000004e-01 7.80000000e-02 2.12900000e+05\n",
      " 1.13100000e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.60000000e-02 2.72000000e-01 7.80000012e-02 2.12900000e+05\n",
      " 1.13100000e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.60000000e-02 2.72000000e-01 7.80000000e-02 2.12900003e+05\n",
      " 1.13100000e+05]\n",
      "RMSE 2.17995317290043\n",
      "[7.60000000e-02 2.72000000e-01 7.80000000e-02 2.12900000e+05\n",
      " 1.13100002e+05]\n",
      "RMSE 2.17995317290043\n"
     ]
    }
   ],
   "source": [
    "best_params, details = leastsq(error_func, params, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fvec</th>\n",
       "      <td>[-0.33610526315786693, -0.378326947375756, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nfev</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fjac</th>\n",
       "      <td>[[-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ipvt</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtf</th>\n",
       "      <td>[-0.33610526315786693, -0.378326947375756, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cov_x</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mesg</th>\n",
       "      <td>The cosine of the angle between func(x) and an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ier</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fvec     [-0.33610526315786693, -0.378326947375756, -0....\n",
       "nfev                                                     6\n",
       "fjac     [[-0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...\n",
       "ipvt                                       [1, 2, 3, 4, 5]\n",
       "qtf      [-0.33610526315786693, -0.378326947375756, -0....\n",
       "cov_x                                                 None\n",
       "mesg     The cosine of the angle between func(x) and an...\n",
       "ier                                                      4\n",
       "dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The cosine of the angle between func(x) and any column of the\\n  Jacobian is at most 0.000000 in absolute value'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details.mesg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best params are only slightly different from the starting place. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.600e-02, 2.720e-01, 7.800e-02, 2.129e+05, 1.131e+05])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the results look like with the `best_params`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17995317290043\n"
     ]
    }
   ],
   "source": [
    "system = make_system(best_params, data)\n",
    "system = remove_units(system)\n",
    "\n",
    "results, details = run_ode_solver(system, slope_func, t_eval=system.ts)\n",
    "Q_frame = recompute_fluxes(results, system)\n",
    "errors = compute_error(Q_frame, data)\n",
    "print(np.sqrt(np.mean(errors**2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RMS error is only slightly smaller.\n",
    "\n",
    "And the results are visually similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_Q_in(Q_frame, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_Q_out(Q_frame, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Try starting the model with a different set of parameters and see if it moves toward the parameters in the paper.\n",
    "\n",
    "I found that no matter where I start, `leastsq` doesn't move far, which suggests that it is not able to optimize the parameters effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes\n",
    "\n",
    "Notes on working with degC.\n",
    "\n",
    "Usually I construct a `Quantity` object by multiplying a number and a unit.  With degC, that doesn't work; you get\n",
    "\n",
    "```\n",
    "OffsetUnitCalculusError: Ambiguous operation with offset unit (degC).\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#16.11 * C "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem is that it doesn't know whether you want a temperature measurement or a temperature difference.\n",
    "\n",
    "You can create a temperature measurement like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16.11 degC"
      ],
      "text/latex": [
       "$16.11\\ \\mathrm{degC}$"
      ],
      "text/plain": [
       "16.11 <Unit('degC')>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = Quantity(16.11, degC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you convert to Kelvin, it does the right thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "289.26 kelvin"
      ],
      "text/latex": [
       "$289.26\\ \\mathrm{kelvin}$"
      ],
      "text/plain": [
       "289.26 <Unit('kelvin')>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.to(K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you subtract temperatures, the results is a temperature difference, indicated by the units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16.11 delta_degC"
      ],
      "text/latex": [
       "$16.11\\ \\mathrm{delta_degC}$"
      ],
      "text/plain": [
       "16.11 <Unit('delta_degC')>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = T - 273.15 * K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you convert a temperature difference to Kelvin, it does the right thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16.11 kelvin"
      ],
      "text/latex": [
       "$16.11\\ \\mathrm{kelvin}$"
      ],
      "text/plain": [
       "16.11 <Unit('kelvin')>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.to(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
